In the framework of progressive pathologies, such as tumors, and notably brain tumors, it is important to be able to appreciate the development of the latter. The determination of the development process of a tumor indeed allows the individual prognosis for a patient to be refined, the therapeutic means to be adjusted according to the aggressivity of the tumor, or also the effectiveness of treatments to be evaluated.
In the particular case of brain pathologies, magnetic resonance imaging, or MRI, is a procedure that is particularly suited to the monitoring of tumors; both for the diagnosis of the tumors prior to any treatment, and for the appreciation of the effectiveness of the treatment. The quantification of the tumor development can therefore be carried out on the basis of a plurality—at least one pair—of MRI images. In practice, it is usual to base such an analysis on longitudinal images coming from two MRI modes: SPGR mode and FLAIR mode, according to the respective acronyms for ‘Spoiled Gradient Recall Echo’ and ‘Fluid Attenuated Inversion Recovery’. It should be noted that other protocols exist for the acquisition of images by MRI, and that the description that follows only relates to these particular acquisition protocols by way of example.
It is then possible for a doctor to apply a manual segmentation of the tumor over a selection of various images, in order to appreciate the spatial extent of the tumor at various cross-sectional levels, at various acquisition times for the images, and in at least one of the two aforementioned MRI modes. Such a job is relatively tedious, and the appreciation of the tumor development according to this method is relatively dependent on the operator, in other words this appreciation exhibits a lack of reproducibility between experts; or worse, this appreciation even exhibits a lack of reproducibility for the same expert. In other words, the same operator may arrive at different conclusions on the basis of identical images. This intra-operator variability is estimated at 15%.
Quantifying the tumor growth is also possible according to the technique, known per se, of the largest diameters. Nevertheless, this method has certain drawbacks. Some of these drawbacks are associated with the orientations of the cross-sectional planes of the images which may be different from one examination to another. In addition, this method is particularly unsuitable following a surgical operation, owing to the presence of the post-operative cavity.
Methods do exist for the automatic determination of the tumor development. Notably, automatic tumor segmentation methods exist, based on relatively complex calculation and image processing algorithms, but which are known to offer relatively poor reliability and robustness. The variability of the parameters relating to the various MRI examinations that the same patient may undergo, for example the operating parameters of the imaging equipment, the position of the head of the patient during the examination, the intrinsic noise phenomena of the imaging apparatus and of its environment, lead to differences in contrast between the images, rendering their direct comparison difficult. Thus, non-linear changes in contrast between sets of images representing volumes of a region of interest do not allow the development of a tumor to be appreciated in a specific manner, on the basis of simple cartographies of differences between these images. These differences in contrast therefore render difficult the automatic comparison between the images. Certain techniques do exist that consist in normalizing the gray levels, and in learning longitudinal anatomical variabilities in noise, then allowing corrections to be applied to the images in order to make them comparable. Nevertheless, these techniques have the drawback of imposing repeated MRI examinations on the patient with the aim of producing precise noise level cartographies. Too large a number of MRI examinations is of course undesirable for reasons of high cost and of stresses imposed on the patient.
In the example of low-grade cerebral gliomae, it is usual to carry out a first MRI examination, followed by an MRI examination three months later, where a classification of the growth of the glioma is carried out. A glioma whose growth after three months is typically greater than 2 millimeters should then be treated as a high-grade glioma, and a glioma whose growth after three months is less than 2 millimeters as a low-grade glioma. This type of pathology typically exhibits relatively weak tumor growths, and hence imposes quantification techniques of millimeter, or even sub-millimeter, precision. It is also particularly advantageous for these techniques to be reproducible, as well as being simple and quick to implement.